In sonar-type systems an array of pressure sensors is utilized to form an acoustic hydrophone. These sensors elements are coupled to the water through the hydrophone face such that the pressure variations at the face of the hydrophone generate electrical signals in the sensor elements. The electrical outputs from each element of the array can be processed separately and then combined to form a narrow directional pattern of maximum sensitivity. A common direction pattern (the boresight beam) has a direction of maximum sensitivity orthogonal to the plane of the array. This orthogonal beam is generated by weighting the output from each element of the array and then summing these weighted signals.
It is frequently desirable in sonar search and tracking modes to alter the direction of maximum sensitivity such that it is no longer orthogonal to the plane of the array, i.e., a tilted beam. The generation of a tilted beam can be accomplished by time shifting the weighted signal outputs from the array before summing these signals. This systematic introduction of a time shift in the outputs of the array elements corresponds to the phase shift that is necessary in order to tilt the beam from its normal boresight axis to a position orthogonal to a vertical plane of the array.
The conventional method of introducing a time shift to the individual elements or groups of elements in an array to obtain a tilted beam is to design a network of high performance time delay circuits for feeding the element output signals to the summer. However, the hardware requirements due to these high performance time delay circuits are significant. If it is desirable to generate and tilt several beams simultaneously, the volume of hardware becomes prohibitive.
Another major problem in this system, and in sonar systems generally, resides in eliciting an optimum signal-to-noise ratio so as to obtain the maximum detection range possible for given hardware with a minimum false alarm rate. One method of providing a high signal-to-noise ratio is to utilize a comb filter comprising a large number of narrowband filters. Each narrowband filter will then compare a target return signal occurring in that frequency band only to the noise in that narrowband frequencies. This design provides a substantial improvement in the signal-to-noise ratio because the target return signal is no longer received against a background of the noise over the entire band of interest. In designing comb filters, it is well known that in order to increase the gain of the filter, the bandwidth for each individual filter must be decreased. A decrease in filter bandwidth, in turn, requires a proportionate increase in the number of filters needed in order for the overall comb to cover the same bandwidth. It can be seen that hardware requirements for such a filter become prohibitive as the filter gain requirements increase.
As an alternative, it has been suggested to use Fourier Transform processing in order to obtain the required large number of narrowband filters or frequency bins for high gain processing. In this regard, Fast Fourier Transforms (FFT) may be used to compute the Fourier Transforms in real-time on the computer. The use of FFT processing can be thought of as providing an order of magnitude over comb filters.
An FFT signal processor operates by sampling amplitude words at prescribed time increments. The interval between input sample points is determined by the sampling theorem which states that for a band-limited signal the samples must be taken on the order of twice the bandwidth. Generally, it is the practice to choose the desired number of frequency bins and then use two samples per bin. Thus, the sampling rate is usually a function of both the bins and the overall system bandwidth, and in most sonar-type signal processing systems is considerably lower than the systems' center frequency.
Two major problems occur when FFT processing is used. First, FFT processing, in practical terms, requires that preliminary circuitry convert the incoming analog signals to digital signals. However, analog-to-digital (A/D) converters are usually capable of handling words with only a limited number of bits. This bit limitation, in turn, severely limits the dynamic range (ratio of the smallest signal to the largest signal) of the input signals applied thereto. Thus, input normalizing or automatic gain control circuitry (AGC) must be used to limit the input signal to a value consistent with the dynamic range of the A/D converter. The operation of the system when the input signal is varying a wide dynamic range will be further limited by the response time of this input normalizing circuitry. The slow response time of this circuitry is due primarily to the requirement that AGC circuitry be less than the system bandwidth in order to prevent the gain from changing faster than the signal and interfering with the information flow. Additionally, the transients generated by the gain changes tend to distort the signal output of the A/D converter. It is possible to increase the dynamic range of the system by increasing the resolution of the A/D converter. However, the conversion time for the A/D converter would also increase substantially due to the higher accuracies required in the A/D converter analog comparisons due to the longer delays needed for the transients in the system to fall below the reduced uncertainty levels.
The second major problem with using FFT processing resides in the fact that the speed of an FFT circuit is usually determined by the multiplication time of the FFT's digital hardware. The FFT system complexity will increase and the system's maximum bandwidth will decrease, when the input word length is increased. Thus, an FFT system is generally a limited dynamic range processor.
The dynamic range limitations of the A/D converter and the FFT circuit are significant because radiated energy detection systems must, in general, have a wide dynamic range so that both very large signals from close targets and very low signals from far targets can be accurately detected.